Methoden vergleichen

	Fake News Detection	Textklassifizierung	TF-IDF
Fachgebiet	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	—	—	1988
Urheber	—	—	Salton & Buckley
Typ	NLP text-classification task	Supervised NLP classification task	Text vectorization / term-weighting scheme
Wegweisende Quelle	Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Aliasnamen	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti	text categorization, document classification, topic classification, metin sınıflandırma	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Verwandt	4	4	3
Zusammenfassung	Fake news detection is a natural-language-processing classification task that assesses the credibility of news text and labels content as fake or genuine. Building on the social-media framing of Shu et al. (2017) and the automated-fact-checking framing of Thorne and Vlachos (2018), it turns unstructured news articles into a supervised credibility decision learned from labelled examples.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.